scholarly journals First Attempt to Couple Proteomics with the AhR Reporter Gene Bioassay in Soil Pollution Monitoring and Assessment

Toxics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 9
Author(s):  
Claudia Landi ◽  
Giulia Liberatori ◽  
Pietro Cotugno ◽  
Lucrezia Sturba ◽  
Maria Luisa Vannuccini ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Cellular Response ◽  
Enrichment Analysis ◽  
Pollution Monitoring ◽  
Translation Regulation ◽  
Chemical Stimulus ◽  
Bioinformatics Analyses ◽  
Promising Tool ◽  
Inorganic Substances ◽  
Complex Scenario

A topsoil sample obtained from a highly industrialized area (Taranto, Italy) was tested on the DR-CALUX® cell line and the exposed cells processed with proteomic and bioinformatics analyses. The presence of polyhalogenated compounds in the topsoil extracts was confirmed by GC-MS/MS analysis. Proteomic analysis of the cells exposed to the topsoil extracts identified 43 differential proteins. Enrichment analysis highlighted biological processes, such as the cellular response to a chemical stimulus, stress, and inorganic substances; regulation of translation; regulation of apoptotic process; and the response to organonitrogen compounds in light of particular drugs and compounds, extrapolated by bioinformatics all linked to the identified protein modifications. Our results confirm and reflect the complex epidemiological situation occurring among Taranto inhabitants and underline the need to further investigate the presence and sources of inferred chemicals in soils. The combination of bioassays and proteomics reveals a more complex scenario of chemicals able to affect cellular pathways and leading to toxicities rather than those identified by only bioassays and related chemical analysis. This combined approach turns out to be a promising tool for soil risk assessment and deserves further investigation and developments for soil monitoring and risk assessment.

scholarly journals Exome sequencing study of 20 patients with high myopia

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5552 ◽  
Author(s):  
Ling Wan ◽  
Boling Deng ◽  
Zhengzheng Wu ◽  
Xiaoming Chen
Keyword(s):  
Exome Sequencing ◽  
Candidate Genes ◽  
High Myopia ◽  
Cellular Response ◽  
Gene Mutations ◽  
Enrichment Analysis ◽  
Zinc Ion ◽  
Ocular Disease ◽  
Disease Genes ◽  
Bioinformatics Analyses

Background High myopia is a common ocular disease worldwide. To expand our current understanding of the genetic basis of high myopia, we carried out a whole exome sequencing (WES) study to identify potential causal gene mutations. Methods A total of 20 individuals with high myopia were exome sequenced. A novel filtering strategy combining phenotypes and functional impact of variants was applied to identify candidate genes by multi-step bioinformatics analyses. Network and enrichment analysis were employed to examine the biological pathways involved in the candidate genes. Results In 16 out of 20 patients, we identified 20 potential pathogenic gene variants for high myopia. A total of 18 variants were located in myopia-associated chromosomal regions. In addition to the novel mutations found in five known myopia genes (ADAMTS18, CSMD1, P3H2, RPGR, and SLC39A5), we also identified pathogenic variants in seven ocular disease genes (ABCA4, CEP290, HSPG2, PCDH15, SAG, SEMA4A, and USH2A) as novel candidate genes. The biological processes associated with vision were significantly enriched in our candidate genes, including visual perception, photoreceptor cell maintenance, retinoid metabolic process, and cellular response to zinc ion starvation. Discussion Systematic mutation analysis of candidate genes was performed using WES data, functional interaction (FI) network, Gene Ontology and pathway enrichment. FI network analysis revealed important network modules and regulator linker genes (EP300, CTNNB1) potentially related to high myopia development. Our study expanded the list of candidate genes associated with high myopia, which increased the genetic screening performance and provided implications for future studies on the molecular genetics of myopia.

Bioinformatics Analysis Reveals Functions of MicroRNAs in Rice Under the Drought Stress

2021 ◽  
Vol 15 (8) ◽  
pp. 927-936 ◽  
Author(s):  
Yan Peng ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Drought Stress ◽  
Target Genes ◽  
Hot Spot ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Promising Tool ◽  
Differentially Expressed Mirnas ◽  
Aba Biosynthesis

Background: Drought is one of the most damaging and widespread abiotic stresses that can severely limit the rice production. MicroRNAs (miRNAs) act as a promising tool for improving the drought tolerance of rice and have become a hot spot in recent years. Objective: In order to further extend the understanding of miRNAs, the functions of miRNAs in rice under drought stress are analyzed by bioinformatics. Method: In this study, we integrated miRNAs and genes transcriptome data of rice under the drought stress. Some bioinformatics methods were used to reveal the functions of miRNAs in rice under drought stress. These methods included target genes identification, differentially expressed miRNAs screening, enrichment analysis of DEGs, network constructions for miRNA-target and target-target proteins interaction. Results: (1) A total of 229 miRNAs with differential expression in rice under the drought stress, corresponding to 73 rice miRNAs families, were identified. (2) 1035 differentially expressed genes (DEGs) were identified, which included 357 up-regulated genes, 542 down-regulated genes and 136 up/down-regulated genes. (3) The network of regulatory relationships between 73 rice miRNAs families and 1035 DEGs was constructed. (4) 25 UP_KEYWORDS terms of DEGs, 125 GO terms and 7 pathways were obtained. (5) The protein-protein interaction network of 1035 DEGs was constructed. Conclusion: (1) MiRNA-regulated targets in rice might mainly involve in a series of basic biological processes and pathways under drought conditions. (2) MiRNAs in rice might play critical roles in Lignin degradation and ABA biosynthesis. (3) MiRNAs in rice might play an important role in drought signal perceiving and transduction.

Groundwater risk assessment of leached inorganic substances from façades coated with plasters and mortars

Chemosphere ◽  
2021 ◽  
pp. 132176
Author(s):  
Pablo Vega-Garcia ◽  
Regina Schwerd ◽  
Sabine Johann ◽  
Brigitte Helmreich
Keyword(s):  
Risk Assessment ◽  
Inorganic Substances

scholarly journals Determining novel candidate anti-hepatocellular carcinoma drugs using interaction networks and molecular docking between drug targets and natural compounds of SiNiSan

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10745
Author(s):  
Qin Zhang ◽  
Zhangying Feng ◽  
Mengxi Gao ◽  
Liru Guo
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Drug Targets ◽  
Cellular Response ◽  
Hormone Receptors ◽  
Enrichment Analysis ◽  
Stage Iii ◽  
Ppi Network ◽  
P53 Signaling

Background SiNiSan (SNS) is an ancient traditional Chinese medicine (TCM) used to treat liver and spleen deficiencies. We studied the unique advantages of using SNS to treat hepatocellular carcinoma (HCC) with multiple components and targets to determine its potential mechanism of action. Methods The active compounds from the individual herbs in the SNS formula and their targets were mined from Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). HCC-associated targets were collected from the TCGA and GEO databases and samples were collected from patients with stage III hepatocellular carcinoma. A compound-disease target network was constructed, visualized, and analyzed using Cytoscape software. We built a protein-protein interaction (PPI) network using the String database. We enriched and analyzed key targets using GSEA, GO, and KEGG in order to explore their functions. Autodock software was used to simulate the process of SNS molecules acting on HCC targets. Results A total of 113 candidate compounds were taken from SNS, and 64 of the same targets were chosen from HCC and SNS. The predominant targets genes were PTGS2, ESR1, CHEK1, CCNA2, NOS2 and AR; kaempferol and quercetin from SNS were the principal ingredients in HCC treatment. The compounds may work against HCC due to a cellular response to steroid hormones and histone phosphorylation. The P53 signaling pathway was significantly enriched in the gene set GSEA enrichment analysis and differential gene KEGG enrichment analysis. Conclusions Our results showed that the SNS component has a large number of stage III HCC targets. Among the targets, the sex hormone receptors, the AR and ESR1 genes, are the core targets of SNS component and the most active proteins in the PPI network. In addition, quercetin, which has the most targets, can act on the main targets (BAX, CDK1, CCNB1, SERPINE1, CHEK2, and IGFBP3) of the P53 pathway to treat HCC.

scholarly journals Identification of potential therapeutic targets for atherosclerosis by analysing the gene signature related to different immune cells and immune regulators in atheromatous plaques

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yang Shen ◽  
Li-rong Xu ◽  
Xiao Tang ◽  
Chang-po Lin ◽  
Dong Yan ◽  
...  
Keyword(s):  
Immune Cells ◽  
Plasma Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gamma Delta ◽  
Hub Genes ◽  
Gene Set Enrichment ◽  
Bioinformatics Analyses ◽  
Immune Infiltration ◽  
Atheromatous Plaques

Abstract Background Atherosclerosis is a chronic inflammatory disease that affects multiple arteries. Numerous studies have shown the inherent immune diversity in atheromatous plaques and suggest that the dysfunction of different immune cells plays an important role in atherosclerosis. However, few comprehensive bioinformatics analyses have investigated the potential coordinators that might orchestrate different immune cells to exacerbate atherosclerosis. Methods Immune infiltration of 69 atheromatous plaques from different arterial beds in GSE100927 were explored by single-sample-gene-set enrichment analysis (presented as ssGSEA scores), ESTIMATE algorithm (presented as immune scores) and CIBERSORT algorithm (presented as relative fractions of 22 types of immune cells) to divide these plaques into ImmuneScoreL cluster (of low immune infiltration) and ImmuneScoreH cluster (of high immune infiltration). Subsequently, comprehensive bioinformatics analyses including differentially-expressed-genes (DEGs) analysis, protein–protein interaction networks analysis, hub genes analysis, Gene-Ontology-terms and KEGG pathway enrichment analysis, gene set enrichment analysis, analysis of expression profiles of immune-related genes, correlation analysis between DEGs and hub genes and immune cells were conducted. GSE28829 was analysed to cross-validate the results in GSE100927. Results Immune-related pathways, including interferon-related pathways and PD-1 signalling, were highly enriched in the ImmuneScoreH cluster. HLA-related (except for HLA-DRB6) and immune checkpoint genes (IDO1, PDCD-1, CD274(PD-L1), CD47), RORC, IFNGR1, STAT1 and JAK2 were upregulated in the ImmuneScoreH cluster, whereas FTO, CRY1, RORB, and PER1 were downregulated. Atheromatous plaques in the ImmuneScoreH cluster had higher proportions of M0 macrophages and gamma delta T cells but lower proportions of plasma cells and monocytes (p < 0.05). CAPG, CECR1, IL18, IGSF6, FBP1, HLA-DPA1 and MMP7 were commonly related to these immune cells. In addition, the advanced-stage carotid plaques in GSE28829 exhibited higher immune infiltration than early-stage carotid plaques. Conclusions Atheromatous plaques with higher immune scores were likely at a more clinically advanced stage. The progression of atherosclerosis might be related to CAPG, IGSF6, IL18, CECR1, FBP1, MMP7, FTO, CRY1, RORB, RORC, PER1, HLA-DPA1 and immune-related pathways (IFN-γ pathway and PD-1 signalling pathway). These genes and pathways might play important roles in regulating immune cells such as M0 macrophages, gamma delta T cells, plasma cells and monocytes and might serve as potential therapeutic targets for atherosclerosis.

scholarly journals Exploring the Mechanism of Skeletal Muscle in a Tacrolimus-Induced Posttransplantation Diabetes Mellitus Model on Gene Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chenlei Zheng ◽  
Cheng Wang ◽  
Tan Zhang ◽  
Ding Li ◽  
Xiao-feng Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Skeletal Muscle ◽  
Muscle Development ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Quadriceps Muscle ◽  
Control Group ◽  
Bioinformatics Analyses

Objective. Posttransplantation diabetes mellitus (PTDM) is a known complication of transplantation that affects the prognosis. Tacrolimus (Tac or FK506) is a widely used immunosuppressant that has been reported to be a risk factor for PTDM and to further induce complications in heart and skeletal muscles, but the mechanism is still largely unknown. In our preliminary experiments, we found that after Tac treatment, blood glucose increased, and the weight of skeletal muscle declined. Here, we hypothesize that tacrolimus can induce PTDM and influence the atrophy of skeletal muscle. Methods. We designed preliminary experiments to establish a tacrolimus-induced PTDM model. Gene expression profiles in quadriceps muscle from this rat model were characterized by oligonucleotide microarrays. Then, differences in gene expression profiles in muscle from PTDM rats that received tacrolimus and control subjects were analyzed by using GeneSpring GX 11.0 software (Agilent). Functional annotation and enrichment analysis of differentially expressed genes (DEGs) helped us identify clues for the side effects of tacrolimus. Results. Our experiments found that the quadriceps in tacrolimus-induced PTDM group were smaller than those in the control group. The study identified 275 DEGs that may be responsible for insulin resistance and the progression of PTDM, including 86 upregulated genes and 199 downregulated genes. GO and KEGG functional analysis of the DEGs showed a significant correlation between PTDM and muscle development. PPI network analysis screened eight hub genes and found that they were related to troponin and tropomyosin. Conclusions. This study explored the molecular mechanism of muscle atrophy in a tacrolimus-induced PTDM model by bioinformatics analyses. We identified 275 DEGs and identified significant biomarkers for predicting the development and progression of tacrolimus-induced PTDM.

Process of risk assessment by research ethics committees: foundations, shortcomings and open questions

2020 ◽  
pp. medethics-2019-105595
Author(s):  
Pranab Rudra ◽  
Christian Lenk
Keyword(s):  
Risk Assessment ◽  
Research Ethics ◽  
Theoretical Approach ◽  
Ethics Committees ◽  
Research Ethics Committees ◽  
Dynamic Framework ◽  
Promising Tool ◽  
Open Questions ◽  
Regulatory Standards ◽  
Risk Definition

Risks and burdens in the study participation, as well as an adequate risk-benefit balance, are key concepts for the evaluation of clinical studies by research ethics committees (RECs). An adequate assessment and continuous monitoring to ensure compliance of risks and burdens in clinical trials have long been described as a central task in research ethics. However, there is currently no uniform and solid theoretical approach to risk assessment by RECs. Regulatory standards of research ethics such as the Declaration of Helsinki provide only minimal guidance on how risk decisions are considered. Due to discrepancies in the existing literature and guidance documents, adequate risk assessment by RECs remains to be elusive. In this article, we address current definitions of risk and present our own concept of aggregate risk definition. Moreover, we highlight the concept of benefit, the standard of reasonableness with respect to ethics literature and different approaches of risk-benefit assessment. In order to present a comprehensive theoretical approach of risk assessment by RECs, further understanding of the definitions of risk may improve adequate decision-making tasks by RECs. To improve the process of risk assessment by RECs, a dynamic framework will be illustrated, showing step-by-step risk assessment functions. This approach may be a promising tool to ensure adequacy in risk assessment by RECs.

scholarly journals Integrated genomics analysis highlights important SNPs and genes implicated in moderate-to-severe asthma based on GWAS and eQTL datasets

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zhouzhou Dong ◽  
Yunlong Ma ◽  
Hua Zhou ◽  
Linhui Shi ◽  
Gongjie Ye ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Severe Asthma ◽  
Antigen Processing ◽  
Association Studies ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Bioinformatics Analyses

Abstract Background Severe asthma is a chronic disease contributing to disproportionate disease morbidity and mortality. From the year of 2007, many genome-wide association studies (GWAS) have documented a large number of asthma-associated genetic variants and related genes. Nevertheless, the molecular mechanism of these identified variants involved in asthma or severe asthma risk remains largely unknown. Methods In the current study, we systematically integrated 3 independent expression quantitative trait loci (eQTL) data (N = 1977) and a large-scale GWAS summary data of moderate-to-severe asthma (N = 30,810) by using the Sherlock Bayesian analysis to identify whether expression-related variants contribute risk to severe asthma. Furthermore, we performed various bioinformatics analyses, including pathway enrichment analysis, PPI network enrichment analysis, in silico permutation analysis, DEG analysis and co-expression analysis, to prioritize important genes associated with severe asthma. Results In the discovery stage, we identified 1129 significant genes associated with moderate-to-severe asthma by using the Sherlock Bayesian analysis. Two hundred twenty-eight genes were prominently replicated by using MAGMA gene-based analysis. These 228 replicated genes were enriched in 17 biological pathways including antigen processing and presentation (Corrected P = 4.30 × 10− 6), type I diabetes mellitus (Corrected P = 7.09 × 10− 5), and asthma (Corrected P = 1.72 × 10− 3). With the use of a series of bioinformatics analyses, we highlighted 11 important genes such as GNGT2, TLR6, and TTC19 as authentic risk genes associated with moderate-to-severe/severe asthma. With respect to GNGT2, there were 3 eSNPs of rs17637472 (PeQTL = 2.98 × 10− 8 and PGWAS = 3.40 × 10− 8), rs11265180 (PeQTL = 6.0 × 10− 6 and PGWAS = 1.99 × 10− 3), and rs1867087 (PeQTL = 1.0 × 10− 4 and PGWAS = 1.84 × 10− 5) identified. In addition, GNGT2 is significantly expressed in severe asthma compared with mild-moderate asthma (P = 0.045), and Gngt2 shows significantly distinct expression patterns between vehicle and various glucocorticoids (Anova P = 1.55 × 10− 6). Conclusions Our current study provides multiple lines of evidence to support that these 11 identified genes as important candidates implicated in the pathogenesis of severe asthma.

scholarly journals Genome-wide transcriptomic analysis reveals correlation between higher WRKY61 expression and reduced symptom severity in Turnip crinkle virus infected Arabidopsis thaliana

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Ruimin Gao ◽  
Peng Liu ◽  
Yuhan Yong ◽  
Sek-Man Wong
Keyword(s):  
Arabidopsis Thaliana ◽  
Transcriptome Analysis ◽  
Plant Immunity ◽  
Plant Defence ◽  
Enrichment Analysis ◽  
Global Gene Expression ◽  
Chemical Stimulus ◽  
Turnip Crinkle Virus ◽  
Rna Seq ◽  
Genome Wide

Abstract Turnip crinkle virus (TCV) is a carmovirus that infects many Arabidopsis ecotypes. Most studies mainly focused on discovery of resistance genes against TCV infection and there is no Next Generation Sequencing based comparative genome wide transcriptome analysis reported. In this study, RNA-seq based transcriptome analysis revealed that 238 (155 up-regulated and 83 down-regulated) significant differentially expressed genes with at least 15-fold change were determined. Fifteen genes (including upregulated, unchanged and downregulated) were selected for RNA-seq data validation using quantitative real-time PCR, which showed consistencies between these two sets of data. GO enrichment analysis showed that numerous terms such as stress, immunity, defence and chemical stimulus were affected in TCV-infected plants. One putative plant defence related gene named WRKY61 was selected for further investigation. It showed that WRKY61 overexpression plants displayed reduced symptoms and less virus accumulation, as compared to wild type (WT) and WRKY61 deficient lines, suggesting that higher WRKY61 expression level reduced TCV viral accumulation. In conclusion, our transcriptome analysis showed that global gene expression was detected in TCV-infected Arabidopsis thaliana. WRKY61 gene was shown to be negatively correlated with TCV infection and viral symptoms, which may be connected to plant immunity pathways.

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